### 1. Imports and class names setup ### 
import gradio as gr
import os
import torch
from model import TinyCNN
from timeit import default_timer as timer
from typing import Tuple, Dict
import torch
import torchvision
from torchvision import transforms
from torch import nn
# Setup class names
with open("class_names.txt", "r") as f: # reading them in from class_names.txt
    class_names = [food_name.strip() for food_name in  f.readlines()]
    
### 2. Model and transforms preparation ###    

# Create model
TinyCNN_model = TinyCNN(input_shape=3, # number of color channels (3 for RGB) 
                  hidden_units=64, 
                  output_shape=len(class_names))

loss_fn = nn.CrossEntropyLoss() # measure how wrong our model is
optimizer = torch.optim.Adam(params = TinyCNN_model.parameters() ,lr=0.001)

transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.Grayscale(num_output_channels=3),
    transforms.ToTensor()
])

# Load saved weights
TinyCNN_model.load_state_dict(
    torch.load(
        f="TinyCNN_3.pth",
        map_location=torch.device("cpu"),  # load to CPU
    )
)

### 3. Predict function ###

# Create predict function
def predict(img) :
    """Transforms and performs a prediction on img and returns prediction and time taken.
    """
    # Start the timer
    start_time = timer()
    
    # Transform the target image and add a batch dimension
    img = transform(img).unsqueeze(dim=0)
    
    # Put model into evaluation mode and turn on inference mode
    TinyCNN_model.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(TinyCNN_model(img), dim=1)
    
    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
    
    # Calculate the prediction time
    pred_time = round(timer() - start_time, 5)
    emoji_list = [["emojis/" + example] for example in os.listdir("emojis")]
    
    emoji_1 =  torch.argmax(pred_probs)
    emoji = class_names[emoji_1]
    if emoji == 'angry':
        a = emoji_list[0]
        a = a[0]
        return pred_labels_and_probs,a
    elif emoji == 'disgust':
        a = emoji_list[1]
        a = a[0]
        return pred_labels_and_probs,a
    elif emoji == 'fear':
        a = emoji_list[2]
        a = a[0]
        return pred_labels_and_probs,a
    elif emoji == 'happy':
        a = emoji_list[3]
        a = a[0]
        return pred_labels_and_probs,a
    elif emoji == 'neutral':
        a = emoji_list[4]
        a = a[0]
        return pred_labels_and_probs,a
    elif emoji == 'sad':
        a = emoji_list[5]
        a = a[0]
        return pred_labels_and_probs,a
    elif emoji == 'surprise':
        a = emoji_list[6]
        a = a[0]
        return pred_labels_and_probs,a
    
    # Return the prediction dictionary and prediction time 


### 4. Gradio app ###

# Create title, description and article strings
title = "Expression Detection"
description = "An app to predict emotions from the list.[Angry, Disgust, Fear, Happy, Neutral, Sad, Surprise]. The model can predict on single face only. So upload an image which has only one face"
article = "Created as a college  project."

# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]

# Create Gradio interface 
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(sources=["upload"], type='pil'),
    outputs=[
        gr.Label(num_top_classes=5, label="Predictions"),
        gr.Image(label="Emotion"),
    ],
    examples=example_list,
    title=title,
    description=description,
    article=article,
)

# Launch the app!
demo.launch()